System and method for monitoring of activity and fall

ABSTRACT

A system for monitoring activity of at least one subject in an environment, comprising at least one sensing assembly, detecting parameters in the environment; and a server communicating with at least one of: i) the subject and ii) the sensing assembly; the at least one sensing assembly comprising at least a first sensor connected to the region of the back of the neck of the subject, the first sensor unit comprising at least one accelerometer.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority on Canadian application no. CA 2,486,949, filed on Dec. 9, 2004. All documents above are herein incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to monitoring of a subject. More specifically, the present invention is concerned with a system and a method for monitoring of activity and fall of a subject.

SUMMARY OF THE INVENTION

More specifically, there is provided a system for monitoring activity of at least one subject in an environment, comprising at least one sensing assembly, detecting parameters in the environment; and a server communicating with at least one of: i) the subject and ii) the at least one sensing assembly; wherein the at least one sensing assembly comprises at least a first sensor connected to the region of the back of the neck of the subject, the first sensor unit comprising at least one accelerometer.

There is further provided a system for monitoring activity of a subject in an environment, comprising at least one sensing assembly, detecting parameters of the given environment; and a server communicating with at least one of: i) the subject and ii) the at least one sensing assembly; wherein the at least one sensing assembly comprises at least a first sensor unit at the back of the neck of the subject.

There is further provided a method for monitoring activity of a subject in an environment, comprising providing at least one sensing assembly in the environment of the subject; providing a server communicating with at least one of: i) the subject and ii) the at least one sensing assembly; generating property vectors from data collected by the at least one sensing assembly; characterizing activity of the subject from the property vectors; and having a result of said characterizing step accessible to the server.

Other objects, advantages and features of the present invention will become more apparent upon reading of the following non-restrictive description of embodiments thereof, given by way of example only with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In the appended drawings:

FIG. 1 is a diagram of a first embodiment of a system according to the present invention;

FIG. 2 is a diagram of a second embodiment of a system according to the present invention;

FIG. 3 is a diagram of a third embodiment of a system according to the present invention;

FIG. 4 is a diagram of a fourth embodiment of a system according to the present invention;

FIG. 5 is a diagram of a fifth embodiment of a system according to the present invention;

FIG. 6 a flowchart of an embodiment of a method according to the present invention;

FIG. 7 is a flowchart of another embodiment of a method according to the present invention;

FIG. 8 is a diagram of positioning obtained by a method according to the present invention;

FIG. 9 illustrates an embodiment of a neck assembly according to the present invention;

FIG. 10 illustrates an embodiment of a wrist assembly according to the present invention;

FIG. 11 is a flowchart of a further embodiment of a method according to the present invention; and

FIG. 12 is a diagram showing the results obtained by a method according to the present invention.

DESCRIPTION OF EMBODIMENTS OF THE INVENTION

A system as illustrated in FIG. 1 comprises a sensing assembly 14 and a server 16, a subject to be monitored 12 being in its environment.

The sensing assembly 14 illustrated in the embodiment of FIG. 1 comprises a first sensor unit 18, located in the region of the neck of the subject 12, for example integrated in a neck assembly worn by the subject 12, and a second sensor unit 20 located in the region of the diaphragm, or/and the fist and/or of the leg of the subject 12, for example in a wrist band.

The sensor units include at least one 2- or 3-axis accelerometers. They may further comprise a gyroscope. The respective number, combination and location of the different sensors depend on target monitoring data, as will be explained hereinbelow.

For example, the first sensor unit 18 may comprise a high G accelerometer and a low G accelerometer, while the second unit sensor 20 comprises a low G accelerometer, both optionally further comprising a gyroscope.

Alternatively, the sensor unit 18, located at the base of the neck of the subject 12, integrated in a neck assembly that the subject 12 wears, may comprise a three-axis high G sensor and a gyroscope. The sensor unit 20, worn as a bracelet, comprises a low G accelerometer and a gyroscope. The sensing assembly 14 communicates with a base 22, located in the environment of the subject 12. This base 22 is connected by a phone link 24 and by Internet 26 to a server 16 for information exchange. Access to the remote server 16 is controlled and allows target persons, such as a physician 28, employees of a health center 30, members of the family 32 of a human subject 12 for example, as well as a call center 34 to monitor data and profiles corresponding to the subject 12 from a distance. The remote server 16 is also used as an interface for sending messages and instructions to the different parts of the system.

The system automatically detects falls and critical activity levels of the subject 12 and is able to emit a request for intervention or alarm, as will be further described hereinbelow.

The base 22 may support a remotely modifiable and programmable reminder function useful for assisting subjects with a cognitive deficiency, whereby remote-intervention functions are allowed. The base 22 may also comprise means for processing data and alarms received from the sensing assembly 14, as well as means for bi-directional voice communication. It may further support a mobile unit of the wireless type offering similar features as just described and optionally integrating GPS localization means allowing monitoring the subject 12 outdoors for example.

In the embodiment illustrated in FIG. 2, the sensor assembly 14 consists of a sensor unit 18 comprising a 3-axis high G sensor and a gyroscope, integrated in a neck assembly worm by the subject 12. The neck assembly comprises RF communication means to the base unit 22, and a device for asking help 36. The base 22 is a hands-free unit allowing wireless communication, through a 2.4 GHz RF link of a Zigbee network for example, to the neck assembly and optional detectors 38. The base 22 includes a help button and a reset button. The base 22 is linked to a remote server 16 by standard telephone network 40.

In the embodiment illustrated in FIG. 3, the base 22 is a free-hands phone allowing wireless communication with the sensor unit 18 and optional detectors 38 on the one hand, and to a remote server 16 via a cellular network 40 on the second hand.

As exemplified in FIG. 1, optional detectors 38 may include for example three-dimensional locaters and interphones (L), motion sensors (M) and presence detectors (D), pillbox sensor (P), smoke detectors (F) and household-appliance detectors (S).

In the embodiment illustrated in FIG. 4, the subject 12 wears a first sensor unit 18 as a neck assembly and a second sensor unit 20 as a wristband. Both sensor units are connected by a unidirectional low frequency low power RF communication link. The sensor unit 20 is connected to a 900 MHz bidirectional receiver 42. The receiver 42, connected to a modem cable or DSL 44, sends data in case of alarm to, or transfer data upon request of, a remote server 16, using an external network accessed either by phone and/or by the Internet.

In the embodiment illustrated FIG. 5, a plurality of sensing assemblies 14 are arranged as a 900 MHz network of the Crossbow type for example, for monitoring a plurality of subjects, for example in a shelter for elderly in the case of human subjects, or a herd. The resulting network of sensing assemblies 14 is linked to a central server 160 connected to other servers 162 used for accessing information provided by the central server 160, and connected to mobile units by a wireless network.

These systems allow collecting data related to the dynamics of the movements of least one subject to be monitored.

The sensor unit 18 in the upper part of the body of a person being monitored, or in the front part of an animal being monitored for example, typically comprises a high G accelerometer to detect fall of the subject. It may further comprise a low G accelerometer to follow the position of movements of the subject. The frequency, velocity and space orientation of movements of the upper trunk of the subject is used to yield movement levels, which can be graded from null to intense. These parameters may be processed using a variety of tools such as fuzzy logic, threshold parameters or a weighting mechanism for example.

For example, energy levels may be obtained as an average of the sum of the absolute values of acceleration along the three axis of the 3 axis accelerometer for example, corrected by an off set characterizing the sum of these accelerations at rest (since accelerometers measure not only the energy involved during a movement of the person, but also the gravitational force the person is submitted to), over a number of measurements per second. This off set is to be taken into account, considering that acceleration generated by a moving person is within the range between about −2 G and +2 G on a one second cycle basis, while the gravitational force is generally about 9.8 m/s/s, i.e. the signal corresponding to the gravitational force may be stronger than those corresponding to the person's movements.

In a particular embodiment, the energy level (NE) are thus obtained as follows: the OG (offset) value of each accelerometer is measured acceleration along the three axis thereof, by placing each axis in perfect alignment with the direction of the gravitational force, yielding the values X_(offset), Y_(offset) and Z_(offset). In practice, the offset values may be set during the fabrication stage of the accelerometer, making for example correspond the OG value to an octet value of 128 (the octet 0 being related to −5 G, and the octet 255 corresponding to +5 G). The energy level NE is calculated as the average over 142 vectorials modules NE_(i) during a period of one second, 142 being the number of sample by second samples, wherein each vectorial module NE_(i) is the square root of the sum of the squared corrected values axis. A new NE value is generated every second and stored. This indicator NE allows quantifying the intensity of movements thus provides energy levels over periods of 1, 5, 30 minutes or more.

A second indicator NM, may be used to quantify the movement levels, describing in particular movements of low amplitude. A detector having a maximum and a minimum on each axis of the accelerometer is used, on 1-second periods of time. By subtracting this minimum to that maximum, the offset is obtained. The NM value is generated and stored simultaneously with the NE.

A gain KM for the movement level NM and a gain Ke for the energy level NE may be defined and used to generate an indicator of movement level.

A third indicator, referred to as INC, may be used to identify a fall event, as detected by an impact sensor, by comparison to an adjustable threshold. The impact sensor measures a gradient and amplitude of shock waves related to a fall, typically characterized by 10 waves over 0.25 seconds. The fall indicator INC may be defined as the sum of absolute values of amplitudes measured during an event. Typically, an INC of 25% corresponds to low amplitude impacts, while sever falls are characterized by INC values of 100% and more.

Such data may further be used to determine sub-levels of sleeping activity, including sleep phases and intensity, or levels of low-intensity activities such as rest or writing process.

The sensor assembly of the present invention allows identifying critical levels of activity, as defined according to a target population of subjects to be monitored, such as persons suffering from functional dependence for example. Critical levels may be set for a range of activities, including a total lack thereof such as in case of death, breathing rhythms and apnea, breathing in absence of minimal movement such as in case of coma or faintness, and hyperactivity.

A preliminary classification of the persons to be monitored according to their degree of physical sufficiency allows setting threshold and control parameters adapted individually to each of these persons and to yield data all the more representative of the state of each one of them.

Nycthemeral or circadian analysis may be used to obtain activity patterns of a subject for time monitoring and identification of abnormal or undesirable variations in time of the subject.

Activity of the subject may be qualitatively assessed, be it walking, feeding or sleeping for example, by analyzing the data collected by the sensing assembly of the system by processing based on neural networks in combination with fuzzy logics or logic threshold values, depending on the processing and memory capacity available.

As described hereinabove, the present system, using at least one sensing assembly comprising at least one a high G accelerometer, may be used for monitoring a fall of this subject and a low-G one for monitoring his-her activity level. Turning to FIGS. 6 to 10 of the appended drawings, embodiments of a method according to the present invention method will now be described.

In FIG. 6, for example, the sensing assembly provides analog signals emitted by at least a 3-axis accelerometer and two gyroscopes. These signals are digitized by an analog-to-digital converter. The obtained data are placed in a memory stack storage used as a short-term memory, and which size defines the term in seconds. Low-level algorithms are applied to the data of the memory stack storage to extract data on the behavior and body posture of the subject being monitored, by generating a property vector from these data. Such property vector includes a number of parameters as follows (see FIG. 7):

-   -   frequency of the body movement, defining the activity of the         monitored subject by the absence of activity, the rest, moderate         and active awaken states;     -   trunk position variation: data collected by the accelerometer         allow determining the space orientation of the accelerometer,         which is related to the space orientation (x, y, z) of the         monitored subject, through gravity and knowledge of the location         of the accelerometer in relation to the body of the monitored         subject (see FIG. 8);     -   height of the monitored subject along a z direction, which may         be used to sort out actual fall events from false alarms by         correlating this height with the position of the body (see FIG.         8);     -   angular velocity of the trunk of the monitored subject, which         may be used to determine whether a trunk position variation         results from a controlled movement or from an accidental         movement;     -   shock wave amplitude, characterizing a fall event;     -   number of shock waves, which may be used to sort out actual fall         events from false alarms.

As shown in FIG. 8, the position of the monitored subject includes his-her position along a vertical axis (noted z in the Figure), which allows assessing the height of an event taking place in a (x,y) plane, i.e. determining whether the monitored subject is lying on the floor, lying on top of a bed, seated, kneeling down, or standing. Measuring the position along the vertical axis may be achieved by RFID, ultrasound or using a camera for example.

The property vector is analyzed to determine whether the monitored subject has fallen and to yield indications on the type of activities the subject is involved in. Fuzzy logics analysis may for example be used to yield to output information, relating to fall and activity respectively.

Using a sensing assembly comprising a sensor unit in the upper part of the subject combining a high frequency-low accuracy (high G in the range of 100 G) accelerometer and a low frequency-high accuracy (low G in the range between 2 and 5 G) accelerometer, allows detecting events such as impact as well as body posture and fine movements.

Moreover, the locations of the sensor units of the sensing assembly relative to the monitored subject's body may be selected to combine a sensor unit at the wrist of the monitored subject with a sensor unit in the region of the base of the neck for example. The combination of these sensor units allows tracking the dynamics of the trunk of the monitored subject while allowing discarding non-pertinent interferences due to non-significant movements for example. Furthermore, this combination allows detecting a fall event while reducing false-alarms generation, since, for example in the case of a single wrist sensor unit, even a knock of the hand wearing the wrist sensor unit on a table for example would be detected as an impact.

Such a combination of the locations of the sensor units allows sorting events, by allowing a validation between impacts or movements usually of lower amplitude of the trunk of the monitored subject and impacts or movements usually of higher amplitude of the arm of the monitored subject. It further allows a qualitative assessment of events, by allowing for example to identify a movement of the arm alone as a protection movement.

The sensor unit in the region of the trunk of the monitored subject may be efficiently connected to the monitored subject without requirement straps, since a neck assembly for example may be used, as described earlier hereinabove.

A gyroscope included in a sensor unit located at the base of the neck or trunk of the monitored subject allows measuring angular velocities, i.e. velocity of lateral movements (left to right and right to left) and back and forth movements of the trunk of the monitored subject. Moreover, data from such gyroscope are used to determine threshold values of angular velocities.

FIG. 9 illustrates an embodiment of a neck assembly 120 of a bolo tie type for locating a sensor unit. A clip 122, besides allowing adjusting a length of the assembly 120 around the neck of the person to be monitored, acts as a balancing weight securing the assembly 120 in place as the monitored person moves. The region of the assembly 120 placed in the back of the neck comprises a flexible unit 124 housing sensors, including a 2-5 G accelerometer 128 and a 50 G accelerometer 126, and optionally a gyroscope 134, a thermistor 130, and an impedance detector 132 for monitoring wearing of the assembly. The assembly 120 comprises an RF antenna 138 and a link 136 between the flexible unit 124 and a pendant 100. The pendant 100 houses a microphone 140, a 3D positioning unit 142, a help summon button 144 and a battery 146.

FIG. 10 illustrates an embodiment of a wrist assembly 150 of a wristwatch band type for locating a sensor unit. The bracelet part 154 integrates the sensors, including a 2-5 G accelerometer 156, a 50 G accelerometer 158, a gyroscope 160, a thermistor 162 and an impedance detector 164 for monitoring wearing of the assembly. The module 152 comprises a microphone 166, a 3D positioning unit 168, a help summon button 170, a battery 172 and a clock 174.

Data from a detector for monitoring wearing of the assembly (132, 164 in FIGS. 9 and 10 for example) may be integrated with data from the accelerometers and other sensors so as to further discriminate between events, between a fall of the monitored person and the monitored person merely dropping the neck assembly 120 or of the wrist assembly 150 on the floor for example.

Such system and method allows identification of critical activity levels, such as coma states, immobility over a period of time, breathing movements interruption, thereby allowing establishing a profile of daily nycthemeral activities of the monitored subject for example. Such profile may be used for detecting sudden variations, which may be significant of a decline in the monitored subject's well being, and provide information concerning the evolution of parameters of the profile of daily nycthemeral activities of the monitored subject, weighted according to the initial functional independence level of the monitored subject to permit assessment of functional independence variations.

Acceleration, velocity and/or position signals sampled on at least one sensing assembly comprising a sensor unit located on the trunk, and, optionally, a sensor unit located on the wrist of the monitored subject, each sensing assembly comprising an accelerometer and optionally a gyroscope and/or a piezo-film, may be used to provide a representation for the behavior of the monitored subject through activity levels (see FIG. 12).

The activity levels are characterized using indicators (such as NE, NM, INC discussed hereinabove for example) on the body posture of the monitored subject and of any change in her-his position in her-his environment, of the velocity and quantity of movements of each part of her-his body wearing a sensor unit, obtained from processing the acceleration, velocity and/or positional signals collected by the sensing assembly. As described hereinabove, these indicators or property vectors are analyzed to yield the state, phase of state and activities of the monitored subject, and the evolution thereof during a predetermined period of time (see FIG. 11).

As illustrated in FIG. 12, the state of the monitored subject may be assessed between a critical state corresponding to a problem or an activity level indicating a potentially deficient well being, and a normal state. In each case, an absence of movement as indicated by the absence of movement detection, may be interpreted as a defective system or as the death of the monitored subject, while slow heart beat, breathing movements and minimal body movements may be interpreted as representative of a rest phase of the monitored subject, and body or member movements, as characterized by their frequency, velocity and orientation, may be evidence of an awareness phase of the monitored subject. In each of these phase, different activity levels may then be assessed, from null in the phase of absence of detection), to apnea (heart beats are detected), coma (breathing is detected), and sleep (movements are detected) in the rest phase, and to low, moderate and high activity levels (movements are detected) in the awareness state.

The frequency analysis of signals collected at the base of the neck of the monitored subject yields a quantitative assessment of the movement of the monitored subject, including the variations of this quantity of movements during short periods of time.

The analysis of signals collected at the wrist of the monitored subject may be combined to yield a qualitative assessment of this movement by incorporating angular velocity measurements and positional measurement in space (x, y, z). The combined analysis of signals emitted in the region of the base of the neck and of signals emitted in the region of the wrist yield an accurate activity profile and efficient positioning along the vertical axis (z). This in turn results in a possible identification of the very room in which an event occurs (bathroom, kitchen, bedroom etc. . . . ) by cross-correlation, and therefore to an increasingly efficient monitoring system, since allowance levels may be pre-determined individually for each room of the subject's habitat, considering for example that the subject detected lying on her-his bed in her-his-bedroom and sleeping is reasonably a normal event.

Therefore the present system and method allow monitoring a subject very precisely in relation to her-his individual functional independence level as well as his-her environment.

Although embodiments were illustrated given hereinabove in relation to a human, the present system and method may efficiently be applied for monitoring a range of subjects, including for example farm animals, domestic pets etc. . . .

Although the present invention has been described hereinabove by way of embodiments thereof, it may be modified, without departing from the nature and teachings of the subject invention as defined in the appended claims. 

1. A system for monitoring activity of at least one subject in an environment, comprising: at least one sensing assembly, detecting parameters in the environment; and a server communicating with at least one of: i) the subject and ii) the sensing assembly; wherein said at least one sensing assembly comprises at least a first sensor unit connected to the region of the back of the neck of the subject, said first sensor unit comprising at least one accelerometer.
 2. The system of claim 1, wherein said first sensor unit is integrated in a neck assembly worn by the subject.
 3. The system of claim 1, wherein said first sensor unit comprises a high G accelerometer.
 4. The system of claim 3, said first sensor unit comprising a low G accelerometer.
 5. The system of claim 1, said first sensor unit comprising at least one of: i) gyroscopes and ii) piezo-films.
 6. The system of claim 1, wherein said at least one sensing assembly comprises a second sensor unit located in at least one region selected from: i) the diaphragm, ii) the wrist and iii) a leg of the subject.
 7. The system of claim 6, wherein said second sensor unit comprises a low G accelerometer.
 8. The system of claim 6, wherein said first sensor unit comprises a high G accelerometer and a gyroscope integrated in a neck assembly that the subject wears; said second sensor unit being worn as a bracelet by the subject; said at least one sensing assembly communicating with a base located in the given environment, said base communicating with said server, access to said server being reserved.
 9. The system of claim 1, wherein said first sensor unit comprises a 3-axis high G sensor, integrated in a neck assembly worm by the subject; said neck assembly comprising RF communication means to a base unit located in said environment; said base unit being linked to said server.
 10. The system of claim 9, wherein said base unit is a hands-free unit in wireless communication with the neck assembly.
 11. The system of claim 9, wherein said base unit further communicates with optional detectors.
 12. The system of claim 6, said first sensor unit being placed in a neck assembly and said second sensor unit being placed in a wristband; said first and second sensor units being connected by a low frequency low power RF communication link.
 13. The system of claim 12, said second sensor unit being connected to a bidirectional receiver in communication with said server.
 14. The system of claim 1, comprising a plurality of sensing assemblies arranged as a network.
 15. A system for monitoring activity of a subject in an environment, comprising: at least one sensing assembly, detecting parameters of the given environment; and a server communicating with at least one of: i) the subject and ii) said at least one sensing assembly; wherein said at least one sensing assembly comprises at least a first sensor unit at the back of the neck of the subject.
 16. The system of claim 15, wherein said at least one sensing assembly combines a high frequency-low accuracy accelerometer and a low frequency-high accuracy accelerometer.
 17. The system of claim 16, wherein said at least one sensing assembly comprises at least one gyroscope.
 18. The system of claim 16, wherein said at least one sensing assembly comprises a detector for monitoring wearing of the assembly.
 19. The system of claim 1, comprising a detector of a position of the subject along a vertical axis.
 20. The system of claim 19, wherein said detector is one of: i) a RFID detector, ii) an ultrasound detector and iii) a camera.
 21. A method for monitoring activity of a subject in an environment, comprising: providing at least one sensing assembly in the environment of the subject; providing a server communicating with at least one of: i) the subject and ii) the at least one sensing assembly; generating property vectors from data collected by the at least one sensing assembly; characterizing activity of the subject from the property vectors; and having a result of said characterizing step accessible to the server.
 22. The method of claim 21, wherein said step of providing at least one sensing assembly comprises providing at least one accelerometer in the region of the back of the neck of the subject; said step of generating property vectors comprising collecting and processing signals emitted by the at least one accelerometer.
 23. The method of claim 21, wherein said step of providing at least one sensing assembly comprises providing a high frequency-low accuracy accelerometer and a low frequency-high accuracy accelerometer in a region of the neck of the subject; said step of generating property vectors comprising collecting and processing signals emitted by the accelerometers.
 24. The method of claim 27, wherein said step of providing at least one sensing assembly further comprises providing a second sensor unit at one of: i) the diaphragm, ii) the wrist and iii) a leg of the subject.
 25. The method of claim 24, wherein said providing a second sensor unit comprises providing a low G accelerometer.
 26. The method of claim 21, wherein said step of generating property vectors comprises generating property vectors containing data related to a number of parameters of the subject, including at least one of: i) frequency of movement of the subject's body; ii) subject's trunk space position variation; iii) subject's body height along a vertical axis; iv) angular velocity of the trunk of the subject; v) shock wave amplitude; and vi) number of shock waves.
 27. The method of claim 21, wherein said step of providing at least one sensing assembly in the environment of the subject comprises combining a first sensor unit at the wrist of the subject and a second sensor unit in the region of the base of the neck of the subject.
 28. The method of claim 25, wherein said step of providing at least one sensing assembly comprises providing a first sensor unit located on a first part of the body of the subject and providing a second sensor unit located on a second part of the body of the subject.
 29. The method of claim 21, wherein said step of providing at least one sensing assembling comprises providing a first sensor unit located on one of: i) the upper trunk and ii) the base of the neck of the subject, and providing a second sensor unit located at one of: i) the diaphragm, ii) the wrist and iii) the leg of the subject.
 30. The method of claim 21, further comprising providing a detector of a position of the subject along a vertical axis.
 31. The method of claim 30, wherein the position of the subject along the vertical axis is one of: I) a RFID detector, ii) an ultrasound detector and iii) a camera.
 32. The method of claim 29, said step of characterizing activity of the subject comprising obtaining at least one of: i) energy levels; ii) movement levels; and iii) impact indicators as obtained using an impact sensor measuring a gradient and amplitude of shock waves.
 33. The system of claim 18, wherein said detector is an impedance detector. 